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Automated Localization of Focal Ventricular Tachycardia From Simulated Implanted Device Electrograms: A Combined Physics–AI Approach

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Sofia Monaci, Karli Gillette, Esther Puyol-Antón, Ronak Rajani, Gernot Plank, Andrew King, Martin Bishop

Original languageEnglish
Article number682446
JournalFrontiers in Physiology
Volume12
DOIs
Published1 Jul 2021

Bibliographical note

Funding Information: Funding. SM was funded by the Engineering and Physical Sciences Research Council (EPSRC; EP/L015226/1). This work was supported by the National Institute for Health Research Biomedical Research Centre at Guy?s and St. Thomas? Trust and King?s College, and the Centre of Excellence in Medical Engineering funded by the Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC; WT 088641/Z/09/Z). MB was also supported by a Medical Research Council New Investigator Grant (MR/N011007/1). Funding Information: SM was funded by the Engineering and Physical Sciences Research Council (EPSRC; EP/L015226/1). This work was supported by the National Institute for Health Research Biomedical Research Centre at Guy’s and St. Thomas’ Trust and King’s College, and the Centre of Excellence in Medical Engineering funded by the Wellcome Trust and Engineering and Physical Sciences Research Council (EPSRC; WT 088641/Z/09/Z). MB was also supported by a Medical Research Council New Investigator Grant (MR/ N011007/1). Publisher Copyright: © Copyright © 2021 Monaci, Gillette, Puyol-Antón, Rajani, Plank, King and Bishop. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

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Abstract

Background: Focal ventricular tachycardia (VT) is a life-threating arrhythmia, responsible for high morbidity rates and sudden cardiac death (SCD). Radiofrequency ablation is the only curative therapy against incessant VT; however, its success is dependent on accurate localization of its source, which is highly invasive and time-consuming. Objective: The goal of our study is, as a proof of concept, to demonstrate the possibility of utilizing electrogram (EGM) recordings from cardiac implantable electronic devices (CIEDs). To achieve this, we utilize fast and accurate whole torso electrophysiological (EP) simulations in conjunction with convolutional neural networks (CNNs) to automate the localization of focal VTs using simulated EGMs. Materials and Methods: A highly detailed 3D torso model was used to simulate ∼4000 focal VTs, evenly distributed across the left ventricle (LV), utilizing a rapid reaction-eikonal environment. Solutions were subsequently combined with lead field computations on the torso to derive accurate electrocardiograms (ECGs) and EGM traces, which were used as inputs to CNNs to localize focal sources. We compared the localization performance of a previously developed CNN architecture (Cartesian probability-based) with our novel CNN algorithm utilizing universal ventricular coordinates (UVCs). Results: Implanted device EGMs successfully localized VT sources with localization error (8.74 mm) comparable to ECG-based localization (6.69 mm). Our novel UVC CNN architecture outperformed the existing Cartesian probability-based algorithm (errors = 4.06 mm and 8.07 mm for ECGs and EGMs, respectively). Overall, localization was relatively insensitive to noise and changes in body compositions; however, displacements in ECG electrodes and CIED leads caused performance to decrease (errors 16–25 mm). Conclusion: EGM recordings from implanted devices may be used to successfully, and robustly, localize focal VT sources, and aid ablation planning.

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